14 citations · 15 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2024★ 1 cited
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
Fedor S. Avilov, Roman A. Eremin, Semen A. Budennyy +1
Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliabilit…
cond-mat.mtrl-sci2022★ 14 cited
Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models
Alexey N. Korovin, Innokentiy S. Humonen, Artem I. Samtsevich +4
The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently…